Medical knowledge bases that capture information about medical entities can be used by reasoning engines and question answering applications to assist medical practitioners. Medical entities are terms representing concepts and events such as diseases, treatments, symptoms and drugs, for example. Typical medical knowledge about these entities includes information about their properties, as well as their relationships with other medical concepts. For example, knowledge about a disease includes its symptoms, treatments, complications and drugs that treat it and includes the relationship of the disease to entities such as drugs and treatments. Similarly, knowledge about a drug includes its relationship to the diseases it treats, its side effects, and its relationship and interactions with other drugs. Thus, relationships between medical entities are needed for constructing comprehensive knowledge bases for them. One way to create knowledge bases is by using a human user encoder to encode his/her knowledge. However, as this process is manually intensive, it is expensive, slow, tedious, and suffers from a lack of wide coverage.
Clinical decision support (CDS) systems acquire data from patient health records and identify and flag potentially adverse drug interactions. Adverse drug interactions may occur due to a wide variety of factors involving active and non-active ingredients of drugs, their mechanisms of actions within the body, their physiological effects, contraindications with certain conditions, among others. While there exist multiple knowledge sources designed for human use, this data is not in a directly machine readable form. Structured knowledge sources like ontologies, conversely, typically lack adequate coverage to build robust CDS systems. Manually encoding knowledge, to make up for this lack of coverage, is both tedious and expensive. A system according to invention principles addresses these deficiencies and related problems.